Abstract

Background Team-based care has consistently been associated with improved clinical outcomes. However, strategies for promoting and sustaining a team-based approach in family medicine practice are more elusive. MethodsWe conducted a longitudinal time series cohort study of 30 primary care providers in seven practices to assess the sequential addition of three different chronic disease management feedback reports over 24 months, culminating in a teambased quality improvement intervention linked to feedback, assessing clinical performance and selfreported effectiveness. ResultsThe proportion of patients at their low density lipoprotein target (

Keywords

Introduction

Team-based approaches to chronic disease management
have been shown to result in better clinical
outcomes.[1–4] However, getting a team to work together
effectively is an active and deliberate process requiring
leadership commitment, development of communication
skills among staff and clearly defined roles and
expectations of team members.[5,6] Much of the research
on integrated team approaches has been in the acute
care setting with much less empirical data informing
primary care practice.[7] The transition to a primary
care medical home model in many care systems has
placed greater urgency on understanding the dynamics
of team-based care, means of engaging patients in
their own care and optimising use of all team members
and clinic time in chronic disease management, disease
prevention and health promotion.[8,9]

Several factors have been identified as critical to a
high functioning clinical team. They include; leadership,
systems support for the clinic team, having a
patient focus, staff education and training, having
ready access to information and embedded process
improvement efforts.[10] Having shared goals that allow
each team member to work to the best of their ability
towards that goal, as well as positive feedback and
support, are equally important. The converse situation
of competing objectives among different team members,
inefficient or intransigent care systems, limited
infrastructure support and no objective feedback fosters
a culture resistant to change and quality improvement.

The role of clinical performance data in developing
and operationalising a team culture is less well understood
but represents a potential opportunity within
the context of quality improvement and microsystems
design. Performance feedback to clinicians and the
clinical team serves several functions. First, it identifies
prioritised outcomes and objectives allowing the clinician
and team to focus energies on metrics given
higher priority by leadership. It also provides a measure
of effectiveness that allows the clinician and clinic
team to assess what they are doing and either continue
it if successful or try alternative approaches if the
outcomes are less than optimal. Finally, it introduces
greater accountability into the care dynamic by reporting
back to the clinician and clinic team measurable
patient outcomes associated with the care they provide.

Having peer comparisons available along with this
feedback also affords the clinician the ability to see
how they are doing relative to their colleagues and
more aggressively address performance linked to outlying
outcomes. This feedback has been well described
in the literature, with most feedback processes directed
towards individual providers and implemented within
the context of pay for performance initiatives.[10–12]
Such processes also typically involve some form of
provider-specific incentive or disincentive for higher
scores in clinical outcomes, productivity and billing,
or some combination. Results from these efforts have
generally been positive, although it has also been argued
that some adverse behaviours (patient dumping, highrisk
case avoidance) may result.[13]

The extent to which these feedback data are used,
either by individual clinicians or by a clinic team, to
impact on care processes depends upon the infrastructure
support within the clinical microsystem for
quality improvement processes to take place. Clinical
microsystems are the basic building blocks of health
care and a platform for providing care and fostering
innovation – they are the small, functional units of
care where patients and families and care teams meet.
The degree to which a clinic team is a microsystem
depends on how well that group is defined, individual
roles are delineated, goals and objectives are articulated
and outcomes specific to that group are measured.[14]
Quality improvement models, such as plan–do–study–
act (PDSA), provide an overarching framework for
testing change ideas and a vehicle for fostering innovation
and improvement efforts. With PDSA there is a
deliberate iterative-feedback approach to quality improvement
that involves identifying a problem and
planning an approach or solution to address it (plan),
carrying out the plan while documenting problems,
observations and data (do), studying the results (study)
and finally making the necessary modifications and
improvements to the care process (act). Finally, the
clinical performance feedback process serves as the driver
for innovation, identifying and prioritising clinical
needs and informing the clinical team about what is
working. To a very large extent, clinical performance
feedback has the potential to serve as the bridge between
the clinical microsystem and quality improvement
initiatives, defining the way clinical teams function.

We present data from a longitudinal time series
cohort study assessing the sequential implementation
of individual and team-based feedback coupled with a
PDSA quality improvement initiative on lipid management
outcomes and team-based care. Survey data
from primary care providers and team nurses on which
approach had the most impact on their individual
practice and on the way their team functioned are also
presented. The findings suggest a specific role for clinical
performance feedback reporting in the development
and functioning of patient-centred medical homes and
for enhanced chronic disease management.

We conducted a longitudinal time series cohort study
correlating organisational changes and different clinician

Methods

We conducted a longitudinal time series cohort study
correlating organisational changes and different clinician performance feedback strategies within the Providence
Veterans Administration (VA) Medical Center
general internal medicine service over a 24-month
period. Approximately 28 000 patients in Rhode Island
and southern Massachusetts were assigned to the
primary care services and received treatment during
the study period in one of seven practice sites, four
located within a tertiary medical centre and three
in community-based outpatient centres (CBOC).
VA Institutional Review Board approval was received
for this study.

Clinical practice characteristics

At the beginning of the study period, the clinical
service was organised as individual practices administratively
grouped into three firms and three community
outpatient clinics, with ancillary clinical services
provided on an as-needed basis to all providers. Six
months into this study the clinical service was reorganised
into integrated clinic teams, each with an
assigned registered nurse (RN), two to three nursing
assistants and four to five primary care providers
caring for approximately 4000 to 4500 patients within
each team.

All patients are assigned to an individual primary
care provider and providers have a pro-rated patient
panel of up to 1200 individuals based on national
Veterans Health Administration (VHA) guidelines,
depending on the amount of clinical time and whether
they are a medical doctor or nurse practitioner. Only
those providers who maintained an active clinical practice
throughout the 24-month study period were
included in the study (n=30).

Clinical performance reporting

The presentation and feedback of clinical performance
data during this time evolved through three phases.
Prior to beginning of the study, all feedback data were
presented as aggregated data at the service level (blood
pressure, HbA1c and lipid control). In Phase 1 of this
study, quarterly clinical performance data specific to
that provider’s panel of patients were presented to
each primary care provider. In Phase 2, the practices
were reorganised into integrated clinical teams and
feedback was provided to the individual provider on
his or her own panel as well as data on how the team
had done overall. This reorganisation was done to
move the practice towards a medical home model of
care and the transition from a physician-driven care
approach to one defined by a team dynamic with
several members of the team (RNs, licensed practical
nurses (LPNs), health technicians) more involved in
direct patient care and care outcomes.

In Phase 3, clinical performance data were presented
at both the individual provider and the clinic
team level (as in Phase 2) but with the requirement
that the data be linked to team-specific quality improvement
initiatives implemented at the start of
Phase 3. It was observed during Phase 2 that even
though clinical performance data were made available
to the entire clinical team and the teams were being
ranked relative to their measured outcomes, few team
members other than the primary care providers identified
with the data or saw it as something to which they
were also accountable. Based on these observations
and anecdotes, we implemented a third phase where
each team was charged with developing a team-based
approach to addressing the clinical performance
measures being reported.

Prior to developing the team-based initiatives we
held a workshop during which the Chronic Care
Model was introduced, including the different process
domains[15,16] and potential roles for different team
members within each of these domains. At this workshop
we also presented the PDSA approach to quality
improvement, with guidance on how it could be applied
to this initiative. The teams were charged with developing
a plan directed towards one of the three chronic
disease management outcomes (low density lipoprotein
(LDL), blood pressure or HbA1c) using their
most recent team report as the baseline for future
comparisons. Table 1 presents a worksheet submitted
by one team, detailing involvement of different team
members, what elements of the Chronic Care Model
were being addressed and what was the targeted goal.

Report data

The provider-specific report included three process
measures (panel size, clinic visits and telephone notes)
and three clinical outcome measures (blood pressure
less than 140/90 mmHg and HbA1c less than 9% in
patients with diabetes and calculated LDL less than
100 mg/dl in patients with either diabetes or coronary
artery disease), as well as a ranking of their clinical
performance relative to that of their peer providers
in primary care. These were reported for all patients
assigned to that provider and/or team during the
previous three months. In addition to the proportion
of patients on their panel who were at target for each of
the clinical outcome measures, the reports also included
a listing of patient names from their panel that
were not on target. The clinical team reports included
only clinical outcome measures of blood pressure
control, HbA1c and LDL data with the proportion
of patients from each team on target along with a
ranking of each team relative to the other six primary
care teams in the clinical service.

The feedback reports were distributed electronically,
with individual primary care provider (PCP)
panel performances entered on an Excel spreadsheet
and team performances and rankings graphically displayed.
Paper copies were also distributed to each
primary care provider, along with aggregated reports
to the PCP and RN team leaders which included a list
of individual patient outliers for each measure. Finally,
the graphical display of team performance and rankings
were posted on bulletin boards near the staff lounge.

Clinician/registered nurse survey

All eligible primary care providers and RNs were
anonymously surveyed as to which feedback or intervention
approach had the greatest impact on the way
they individually practiced and how their team functioned
with regard to communication and coordination
of care among team members. The feedback/intervention
query fields were: 1) facility-specific reports;
2) provider-specific reports with patient outliers
listed; 3) team-based reports and rankings along
with patient outliers listed; and 4) team-based reports
linked to quality improvement initiatives. Providers
(n=30) and RNs (n=7) were asked to rank order each
choice relative to the others, with a text section for
comments. The survey was conducted anonymously
in order to minimise any bias towards socially
favourable responses or perceived coercion.

Data capture

Data were identified from the electronic medical record
(the VHA Computerized Patient Record System) using
Microsoft Proclarity (Microsoft, Seattle, WA) software
for all patients assigned to primary care providers in
the study. Laboratory values drawn at facilities outside
the VA were manually entered by the provider into the
electronic medical record and subsequently abstracted
by the same process. Tests not done within the specified
time period (12 months for a patient with comorbid
diabetes or coronary artery disease) or outside lab
values drawn by non-VA facilities that were not entered
were considered out of range or missing. While the
clinical reporting process included lipid levels, HbA1c
and blood pressure results, only the LDL values are
presented in this analysis. This was done because the
clinical service was already at or above target performance
levels for both blood pressure and HbA1c control
at the start of this study providing a more limited
margin for measured improvement. In addition, the
time taken for HbA1c values to change is longer than
that for LDL values while blood pressures recorded in
this process included readings captured throughout
the hospital, including the emergency department and
other settings where temporal pain or an acute illness
may have affected the readings. Of note, there were
improvements in all three areas although the difference
was greatest for the LDL measure.

Data analysis

Temporal data are reported as a proportion of patients
with diabetes or coronary artery disease who were at
LDL target (<100 mg/dl) at each quarterly interval.
These proportions are reported for both overall primary
care enrolment and by each individual provider
panel. Clinical performance data from the beginning
and end of Phase 1 (six months)were compared to assess
changes associated with the transition from facilitybased
to provider-specific reporting. Similarly, clinical
performance measures from the end of Phase 1 are
compared with data from the end of Phase 2 (nine
months) to assess changes associated with the transition
to team-based care along with individual provider
and team-level feedback. Lipid management outcomes
from the end of Phase 2 are compared with the end of
Phase 3 (nine months) to assess changes associated
with the transition from team-based reporting to team
reporting linked to PDSA quality improvement initiatives.
Clinical outcome measures are also compared
from the beginning of Phase 1 to the end of Phase 3 in
order to assess overall performance. Factor analyses of
provider practice characteristics (panel size, number of
clinic visits and telephone notes generated each quarter
etc.) were also considered in relationship to LDL
target performance. Stata 8.0 software was used in the
analyses and statistical significance is reported as z
statistic for proportions of dichotomous variables
with a P<0.05 (Stata Corp, College Station, TX). Survey
data were aggregated and rank ordered with text field
comments separately summarised and reported.

Results

The overall number of patients with either diabetes or
coronary artery disease considered in this study
ranged from 9810 to 10 405 each quarter, depending
on shifts in enrolment, patients moving out of the area
or deaths (see Table 2). Approximately two-thirds of
patients were managed at the medical centre by 22
primary care providers, while the remaining group
was managed in the three community outpatient
centres by eight primary care providers.

Proportion of patients at target LDL

There was an overall increase in the proportion of
patients with LDL values less than 100 mg/dl during
the 24 months of data reporting. As shown in Figure 1,the proportion of patients on target was 63.6% at the
beginning of the observation period and increased to
69.3% (P<0.001) by the end of eight quarters. During
Phase 1 (provider-specific data only) there was an
increase in the proportion of patients reaching the
LDL target of 1.4%. In Phase 2 (team-based and
individual provider data and ranking; concurrent
transition from three firms to four medical teams
within the medical centre) there was an overall improvement
of 1.2%. Phase 3 (team-based data linked
to quality improvement plans, individual provider
data and ranking) was notable for the largest increase
in the proportion of patients on target for LDL
management, with a net increase of 3.1% from the
end of Phase 2 to the end of Phase 3 (Figure 1).

Figure 1: Aggregated proportion of patients at LDL target

Provider-specific performance

Figure 2 represents the proportion of patients achieving
their LDL target within each provider panel during the eight reporting periods, with the number at or
above the VA goal of 68% depicted above the horizontal
bar. In the first reporting period, only eight
providers (26.7%) were at or above the VA goal of
68%. By reporting period eight, the proportion at or
above goal was 60% with most of the increase occurring
the last three quarters, when the quality improvement
initiatives were underway.

Figure 2: PCP panels with proportion of patients at LDL target

There was no association between panel size and
whether the panel was at or above capacity and between
LDL performance or trend improvement in LDL
performance. Nor were there any differences noted
based on whether the provider was a nurse practitioner
or medical doctor. Those providers that registered
at least 20 telephone calls to their patients in the
previous quarter were more likely to register a significant
improvement in LDL performance and to be at or
above the VHA national goal (P=0.04).

Survey responses

The overall response rate was 73.3% for primary care
providers and 71.4% for nurses. The provider-specific
report with patient outliers was rated by 77.3% of
respondents as having the most impact on individual
practice approach compared with the three other
options (facility-specific reports, team reports and
rankings and team reports linked to QI initiatives),
while 86.4% rated it as either first or second. This was
also reported as having the most impact on teambased
care and coordination, with 68.2% rating it as
first and 72.7% ranking it first or second. Team-based
reports and rankings were ranked second by most
providers in relation to impact on individual practices
(68.2%) and second in relation to impact on team care
and coordination (72.7%). Fewer respondents ranked
team-based reporting linked to quality improvement
initiatives as having a major impact on either their
individual practice approach (with 45.4% of respondents
ranking it first or second) or surprisingly as
having an impact on team-based care and coordination
(with 50% ranking it first or second for teambased care
and coordination). Facility-aggregated reports were
overwhelmingly ranked fourth for both questions.
The nurses’ rankings were consistent with the primary
care providers’, except that slightly more nurses rated
the team reports linked to quality improvement initiatives
as having an impact on individual and teambased
functioning, but this still followed individual
and team-based reports.

‘The reports helped me keep track of patients I might have
lost track of .’

Comments specific to the quality improvement PDSA
included:

‘The project helped focus us on a plan and who would do
what.’

Discussion

How well clinical teams work together is as much a
function of who is on the team and how they are
organised as of what they are tasked to do. In this
longitudinal time series cohort study of incremental
implementation of a patient-centred medical home
model, reorganising into teams alone had a nominal
impact on chronic disease management. This was
despite directed clinical feedback, clearly articulated
clinical service priorities and structural modelling to
optimise team-based care (team meetings, engaging
all team members, skill-building workshops etc.).
More robust clinical performance outcomes occurred
only when care planning was organised around a
quality improvement PDSA-based initiative. This process
appeared to give the teams a more formalised
structure for engaging the full complement of team
members in tasks specific to the team-identified goal.
Ironically, five of the seven teams specifically focused
on an LDL management goal (the others concentrating
on HbA1c or blood pressure goals), but they
all noted some improvements in lipid management,
reflecting a spillover effect from employing this process.

These findings are consistent with those previously
noted in the literature. Practices more actively engaged
in chronic disease management and applying the
Chronic CareModel have consistently been associated
with enhanced clinical performance.17 Strategies employed
by these clinics include using patient registries,
patient prompts, clinical reminders and reports, applying
evidence-based guidelines to care, engaging
patients in their own self-care and employing community
resources, all core elements of the Chronic
Care Model.[15,16] Hysong and colleagues found that
facilities with high rates of compliance with clinical
practice guidelines were more likely to provide timely,
individualised, non-punitive feedback to providers.[18]
A literature review of interventions to improve team
effectiveness found positive results associated with
simulation, Crew resource management training,
team-based training and projects on continuous quality
improvement.[7] Continuous quality improvement
interventions have also been associated with improvements
in primary care management in a randomised
controlled trial involving 49 practices in the
Netherlands.19 In our study, the incremental addition
of individual and team-based feedback was associated
with modest improvements in lipid management that
increased substantially when linked to team-based
quality improvement initiatives.

Interestingly, primary care providers and nurses
responding to the survey consistently rated the individual
report with peer ranking as having a greater influence
on how they practiced and how they coordinated care
with their team. The team-based reports linked to
quality improvement initiatives were rated much lower
despite the fact that the most robust clinical improvements
occurred when these reports were initiated.
While the study design does not allow us to determine
the independent effect of the different clinical performance
feedback reports, there are three possible explanations for our findings. First, the survey was
conducted about six months after the PDSA cycle
concluded and it is possible that the PDSA-developed
interventions had already been incorporated into
standard clinical practice with only nominal attribution
given to any role they may have played. The
fact that the performance gains achieved during this
phase continued for 12 months following the study
conclusion would support this. Second, it is also
possible that the robust improvement noted in Phase
3 compared with Phases 1 and 2 reflect acclimatisation
to the individual reports/rankings as suggested by the
clinician survey results and the noted effect occurred
independent of the PDSA process. The nurses in this
study who prior to the PDSA process had not been
involved in chronic disease management protocols
cited similar benefits from the feedback reports which
would not support this explanation. Finally, it is
possible that the different reports address two distinct
dimensions of continuous quality improvement. The
reporting linked to the PDSA cycle addresses a microsystems
design/care processing approach to quality
improvement while the individual and team-based
reports focus on enhancing provider and team-member
accountability and ‘ownership’ of outcomes. Both
represent critical elements for practice innovation,
adoption and sustainability.

Our findings do suggest that linking performance
measures to team-based quality improvement initiatives
serves to codify and reinforce practice principles
consistent with a Chronic Care Model. Finally, it is
important to note that those providers with more than
[20] calls to patients outside of clinic visits during a
reporting period were more likely to have better LDL
management performance. This out-of-clinic activity
reflects an element of planned care espoused in the
patient-centred medical home model and is likely to
be an accurate metric of better performing clinical
units.

There are several limitations to consider and address.
First, the data are limited to one clinical department in
the north-east USA serving a veteran population. It is
unclear whether these data are replicable in other
geographic settings or with other population groups.
However, it is important to note that the outcomes
reported exceed Healthcare Effectiveness Data and
Information Set (HEDIS) results found in Medicare
and private health plans and occurred in a veteran
population considered as more challenging in their
burden of chronic disease care.[20,21] The infrastructure
within the VA system with limited panel sizes, an
integrated care system, robust primary care service
line structure and efficient electronic medical record
system all facilitate the implementation of the Chronic
Care Model and chronic disease management initiatives.
Replicating this in settings without the established
infrastructure and the comprehensive electronic
health record that is afforded within the VA health
system22 may be more challenging.

Second, it is possible that there were other care
trends or factors taking place that influenced our
outcomes. However, there were no changes to the
drug formulary during this time, only providers who
maintained panels throughout the course of the study
were included in the analysis and there were no major
influxes or effluxes of patients to influence the denominator
significantly. An important limitation described
earlier is that the different clinical performance reports
were implemented sequentially and were cumulative,
making it difficult to determine with any degree of
certainty any independent effect from a specific
reporting process or metric. Finally, the study period
was [24] months with eight quarterly reporting cycles. It
is possible that these changes are non-sustainable
beyond this period of scrutiny and reflect an observer
effect that the reporting and clinical improvement
initiatives prompted. To counter this,we continued to
monitor and report quarterly data and have not noted
a drop-off in clinical performance in the 12 months
following completion of this study.

In summary, reporting of clinical performance data
can promote and reflect significantly improved clinical
outcomes when the data is reported to clinical
teams and when it is used to drive clinical improvement
initiatives.